Overview

Dataset statistics

Number of variables42
Number of observations1920
Missing cells38065
Missing cells (%)47.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory630.1 KiB
Average record size in memory336.1 B

Variable types

Numeric10
Categorical16
Boolean2
Unsupported14

Alerts

検査種別 has constant value "Vascular IVR"Constant
23動脈瘤データ_Ver2（An ID 1）::術中Rupture has constant value "True"Constant
検査日 has a high cardinality: 1175 distinct valuesHigh cardinality
治療時合併症コメント has a high cardinality: 54 distinct valuesHigh cardinality
An status 1 is highly imbalanced (98.8%)Imbalance
19動脈瘤データ（An ID 1）::Aneurysm Type2 is highly imbalanced (95.0%)Imbalance
再治療 is highly imbalanced (78.2%)Imbalance
治療回数 is highly imbalanced (98.3%)Imbalance
治療時合併症 is highly imbalanced (80.8%)Imbalance
19動脈瘤データ（An ID 1）::bleb is highly imbalanced (62.5%)Imbalance
Side has 109 (5.7%) missing valuesMissing
VABAの区分 has 1695 (88.3%) missing valuesMissing
Location2 has 235 (12.2%) missing valuesMissing
aneurysm size3 has 1287 (67.0%) missing valuesMissing
VER has 1727 (89.9%) missing valuesMissing
outcome has 361 (18.8%) missing valuesMissing
治療時合併症コメント has 1714 (89.3%) missing valuesMissing
23動脈瘤データ_Ver2（An ID 1）::術中Date of rupture has 1896 (98.8%) missing valuesMissing
23動脈瘤データ_Ver2（An ID 1）::術中Rupture has 1896 (98.8%) missing valuesMissing
total length has 244 (12.7%) missing valuesMissing
Unnamed: 28 has 1920 (100.0%) missing valuesMissing
Unnamed: 29 has 1920 (100.0%) missing valuesMissing
Unnamed: 30 has 1920 (100.0%) missing valuesMissing
Unnamed: 31 has 1920 (100.0%) missing valuesMissing
Unnamed: 32 has 1920 (100.0%) missing valuesMissing
Unnamed: 33 has 1920 (100.0%) missing valuesMissing
Unnamed: 34 has 1920 (100.0%) missing valuesMissing
Unnamed: 35 has 1920 (100.0%) missing valuesMissing
Unnamed: 36 has 1920 (100.0%) missing valuesMissing
Unnamed: 37 has 1920 (100.0%) missing valuesMissing
Unnamed: 38 has 1920 (100.0%) missing valuesMissing
Unnamed: 39 has 1920 (100.0%) missing valuesMissing
Unnamed: 40 has 1920 (100.0%) missing valuesMissing
Unnamed: 41 has 1920 (100.0%) missing valuesMissing
An ID1 is uniformly distributedUniform
検査日 is uniformly distributedUniform
23動脈瘤データ_Ver2（An ID 1）::術中Date of rupture is uniformly distributedUniform
An ID1 has unique valuesUnique
Unnamed: 28 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 29 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 30 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 31 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 32 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 33 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 34 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 35 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 36 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 38 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 39 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 40 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 41 is an unsupported type, check if it needs cleaning or further analysisUnsupported
aneurysm volume has 1284 (66.9%) zerosZeros

Reproduction

Analysis started2023-09-16 06:32:33.038588
Analysis finished2023-09-16 06:32:58.309509
Duration25.27 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

患者ID
Real number (ℝ)

Distinct1687
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9354855.4
Minimum34801
Maximum98309706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:32:58.407519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum34801
5-th percentile2435699
Q17500926.8
median9968041
Q311630790
95-th percentile12795461
Maximum98309706
Range98274905
Interquartile range (IQR)4129863.5

Descriptive statistics

Standard deviation4280167.3
Coefficient of variation (CV)0.45753431
Kurtosis177.78453
Mean9354855.4
Median Absolute Deviation (MAD)1968958.5
Skewness8.8854465
Sum1.7961322 × 1010
Variance1.8319832 × 1013
MonotonicityNot monotonic
2023-09-16T15:32:58.616751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11408853 5
 
0.3%
7496774 4
 
0.2%
11771184 3
 
0.2%
11474442 3
 
0.2%
7716509 3
 
0.2%
1489561 3
 
0.2%
8496665 3
 
0.2%
7742909 3
 
0.2%
12913181 3
 
0.2%
7432003 3
 
0.2%
Other values (1677) 1887
98.3%
ValueCountFrequency (%)
34801 1
0.1%
35688 1
0.1%
59395 1
0.1%
72414 1
0.1%
101339 1
0.1%
109992 1
0.1%
122253 1
0.1%
150044 1
0.1%
170224 1
0.1%
175063 1
0.1%
ValueCountFrequency (%)
98309706 1
0.1%
91755313 1
0.1%
60251105 1
0.1%
20120828 1
0.1%
20110729 1
0.1%
13070516 1
0.1%
13070277 1
0.1%
13063784 1
0.1%
13056952 1
0.1%
13055007 1
0.1%

An ID1
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1920
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14038.554
Minimum13073
Maximum15002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:32:58.857784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13073
5-th percentile13168.95
Q113554.75
median14039.5
Q314521.25
95-th percentile14905.05
Maximum15002
Range1929
Interquartile range (IQR)966.5

Descriptive statistics

Standard deviation557.3519
Coefficient of variation (CV)0.039701518
Kurtosis-1.2006663
Mean14038.554
Median Absolute Deviation (MAD)483.5
Skewness-0.0048952207
Sum26954024
Variance310641.15
MonotonicityStrictly increasing
2023-09-16T15:32:59.049185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13073 1
 
0.1%
13074 1
 
0.1%
14369 1
 
0.1%
14368 1
 
0.1%
14367 1
 
0.1%
14366 1
 
0.1%
14365 1
 
0.1%
14364 1
 
0.1%
14363 1
 
0.1%
14362 1
 
0.1%
Other values (1910) 1910
99.5%
ValueCountFrequency (%)
13073 1
0.1%
13074 1
0.1%
13075 1
0.1%
13076 1
0.1%
13077 1
0.1%
13078 1
0.1%
13079 1
0.1%
13080 1
0.1%
13081 1
0.1%
13082 1
0.1%
ValueCountFrequency (%)
15002 1
0.1%
15001 1
0.1%
14999 1
0.1%
14998 1
0.1%
14997 1
0.1%
14996 1
0.1%
14995 1
0.1%
14994 1
0.1%
14993 1
0.1%
14992 1
0.1%

検査日
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1175
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2017/5/31
 
7
2010/12/21
 
5
2018/5/16
 
5
2018/12/12
 
5
2011/12/20
 
5
Other values (1170)
1893 

Length

Max length10
Median length9
Mean length8.9375
Min length8

Characters and Unicode

Total characters17160
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique670 ?
Unique (%)34.9%

Sample

1st row2003/5/14
2nd row2003/7/9
3rd row2003/7/11
4th row2003/11/28
5th row2004/10/19

Common Values

ValueCountFrequency (%)
2017/5/31 7
 
0.4%
2010/12/21 5
 
0.3%
2018/5/16 5
 
0.3%
2018/12/12 5
 
0.3%
2011/12/20 5
 
0.3%
2010/7/7 5
 
0.3%
2012/3/13 5
 
0.3%
2021/2/19 5
 
0.3%
2021/4/14 5
 
0.3%
2008/8/26 5
 
0.3%
Other values (1165) 1868
97.3%

Length

2023-09-16T15:32:59.264721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017/5/31 7
 
0.4%
2021/2/19 5
 
0.3%
2010/12/21 5
 
0.3%
2019/8/13 5
 
0.3%
2015/6/30 5
 
0.3%
2008/8/26 5
 
0.3%
2021/4/14 5
 
0.3%
2015/1/20 5
 
0.3%
2012/3/13 5
 
0.3%
2010/7/7 5
 
0.3%
Other values (1165) 1868
97.3%

Most occurring characters

ValueCountFrequency (%)
/ 3840
22.4%
2 3378
19.7%
1 3042
17.7%
0 2891
16.8%
8 608
 
3.5%
6 604
 
3.5%
9 598
 
3.5%
7 594
 
3.5%
3 584
 
3.4%
4 540
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13320
77.6%
Other Punctuation 3840
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3378
25.4%
1 3042
22.8%
0 2891
21.7%
8 608
 
4.6%
6 604
 
4.5%
9 598
 
4.5%
7 594
 
4.5%
3 584
 
4.4%
4 540
 
4.1%
5 481
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 3840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 3840
22.4%
2 3378
19.7%
1 3042
17.7%
0 2891
16.8%
8 608
 
3.5%
6 604
 
3.5%
9 598
 
3.5%
7 594
 
3.5%
3 584
 
3.4%
4 540
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 3840
22.4%
2 3378
19.7%
1 3042
17.7%
0 2891
16.8%
8 608
 
3.5%
6 604
 
3.5%
9 598
 
3.5%
7 594
 
3.5%
3 584
 
3.4%
4 540
 
3.1%

検査種別
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
Vascular IVR
1920 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters23040
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVascular IVR
2nd rowVascular IVR
3rd rowVascular IVR
4th rowVascular IVR
5th rowVascular IVR

Common Values

ValueCountFrequency (%)
Vascular IVR 1920
100.0%

Length

2023-09-16T15:32:59.474388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:32:59.629334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
vascular 1920
50.0%
ivr 1920
50.0%

Most occurring characters

ValueCountFrequency (%)
V 3840
16.7%
a 3840
16.7%
s 1920
8.3%
c 1920
8.3%
u 1920
8.3%
l 1920
8.3%
r 1920
8.3%
1920
8.3%
I 1920
8.3%
R 1920
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13440
58.3%
Uppercase Letter 7680
33.3%
Space Separator 1920
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3840
28.6%
s 1920
14.3%
c 1920
14.3%
u 1920
14.3%
l 1920
14.3%
r 1920
14.3%
Uppercase Letter
ValueCountFrequency (%)
V 3840
50.0%
I 1920
25.0%
R 1920
25.0%
Space Separator
ValueCountFrequency (%)
1920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21120
91.7%
Common 1920
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 3840
18.2%
a 3840
18.2%
s 1920
9.1%
c 1920
9.1%
u 1920
9.1%
l 1920
9.1%
r 1920
9.1%
I 1920
9.1%
R 1920
9.1%
Common
ValueCountFrequency (%)
1920
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V 3840
16.7%
a 3840
16.7%
s 1920
8.3%
c 1920
8.3%
u 1920
8.3%
l 1920
8.3%
r 1920
8.3%
1920
8.3%
I 1920
8.3%
R 1920
8.3%

08個人情報(本院)::性別
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
女
1473 
男
447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row女
2nd row女
3rd row女
4th row女
5th row女

Common Values

ValueCountFrequency (%)
女 1473
76.7%
男 447
 
23.3%

Length

2023-09-16T15:32:59.757706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:32:59.915375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
女 1473
76.7%
男 447
 
23.3%

Most occurring characters

ValueCountFrequency (%)
女 1473
76.7%
男 447
 
23.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1920
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
女 1473
76.7%
男 447
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
Han 1920
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
女 1473
76.7%
男 447
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 1920
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
女 1473
76.7%
男 447
 
23.3%

年齢
Real number (ℝ)

Distinct66
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.889583
Minimum11
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:00.093976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile40.95
Q152
median62
Q370
95-th percentile78
Maximum89
Range78
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.897613
Coefficient of variation (CV)0.19539653
Kurtosis-0.20126666
Mean60.889583
Median Absolute Deviation (MAD)9
Skewness-0.34303722
Sum116908
Variance141.5532
MonotonicityNot monotonic
2023-09-16T15:33:00.301093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 68
 
3.5%
64 64
 
3.3%
70 63
 
3.3%
50 63
 
3.3%
60 62
 
3.2%
62 60
 
3.1%
67 58
 
3.0%
63 57
 
3.0%
68 56
 
2.9%
71 56
 
2.9%
Other values (56) 1313
68.4%
ValueCountFrequency (%)
11 1
 
0.1%
21 1
 
0.1%
22 2
 
0.1%
24 1
 
0.1%
25 4
0.2%
29 5
0.3%
30 1
 
0.1%
31 1
 
0.1%
32 5
0.3%
33 3
0.2%
ValueCountFrequency (%)
89 2
 
0.1%
88 3
 
0.2%
87 6
0.3%
86 3
 
0.2%
85 3
 
0.2%
84 5
 
0.3%
83 14
0.7%
82 10
0.5%
81 10
0.5%
80 12
0.6%

An status 1
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
unrupture
1918 
rupture
 
2

Length

Max length9
Median length9
Mean length8.9979167
Min length7

Characters and Unicode

Total characters17276
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunrupture
2nd rowunrupture
3rd rowunrupture
4th rowunrupture
5th rowunrupture

Common Values

ValueCountFrequency (%)
unrupture 1918
99.9%
rupture 2
 
0.1%

Length

2023-09-16T15:33:00.670449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:00.855698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
unrupture 1918
99.9%
rupture 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
u 5758
33.3%
r 3840
22.2%
p 1920
 
11.1%
t 1920
 
11.1%
e 1920
 
11.1%
n 1918
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17276
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 5758
33.3%
r 3840
22.2%
p 1920
 
11.1%
t 1920
 
11.1%
e 1920
 
11.1%
n 1918
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 17276
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 5758
33.3%
r 3840
22.2%
p 1920
 
11.1%
t 1920
 
11.1%
e 1920
 
11.1%
n 1918
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 5758
33.3%
r 3840
22.2%
p 1920
 
11.1%
t 1920
 
11.1%
e 1920
 
11.1%
n 1918
 
11.1%

19動脈瘤データ（An ID 1）::Aneurysm Type2
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
saccular
1903 
fusiform
 
14
dissecting
 
3

Length

Max length10
Median length8
Mean length8.003125
Min length8

Characters and Unicode

Total characters15366
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsaccular
2nd rowsaccular
3rd rowsaccular
4th rowsaccular
5th rowsaccular

Common Values

ValueCountFrequency (%)
saccular 1903
99.1%
fusiform 14
 
0.7%
dissecting 3
 
0.2%

Length

2023-09-16T15:33:00.994869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:01.174488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
saccular 1903
99.1%
fusiform 14
 
0.7%
dissecting 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
c 3809
24.8%
a 3806
24.8%
s 1923
12.5%
u 1917
12.5%
r 1917
12.5%
l 1903
12.4%
f 28
 
0.2%
i 20
 
0.1%
o 14
 
0.1%
m 14
 
0.1%
Other values (5) 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15366
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 3809
24.8%
a 3806
24.8%
s 1923
12.5%
u 1917
12.5%
r 1917
12.5%
l 1903
12.4%
f 28
 
0.2%
i 20
 
0.1%
o 14
 
0.1%
m 14
 
0.1%
Other values (5) 15
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 15366
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 3809
24.8%
a 3806
24.8%
s 1923
12.5%
u 1917
12.5%
r 1917
12.5%
l 1903
12.4%
f 28
 
0.2%
i 20
 
0.1%
o 14
 
0.1%
m 14
 
0.1%
Other values (5) 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 3809
24.8%
a 3806
24.8%
s 1923
12.5%
u 1917
12.5%
r 1917
12.5%
l 1903
12.4%
f 28
 
0.2%
i 20
 
0.1%
o 14
 
0.1%
m 14
 
0.1%
Other values (5) 15
 
0.1%

Side
Categorical

Distinct2
Distinct (%)0.1%
Missing109
Missing (%)5.7%
Memory size15.1 KiB
left
990 
right
821 

Length

Max length5
Median length4
Mean length4.4533407
Min length4

Characters and Unicode

Total characters8065
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowleft
4th rowright
5th rowleft

Common Values

ValueCountFrequency (%)
left 990
51.6%
right 821
42.8%
(Missing) 109
 
5.7%

Length

2023-09-16T15:33:01.323007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:01.482698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
left 990
54.7%
right 821
45.3%

Most occurring characters

ValueCountFrequency (%)
t 1811
22.5%
l 990
12.3%
e 990
12.3%
f 990
12.3%
r 821
10.2%
i 821
10.2%
g 821
10.2%
h 821
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8065
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1811
22.5%
l 990
12.3%
e 990
12.3%
f 990
12.3%
r 821
10.2%
i 821
10.2%
g 821
10.2%
h 821
10.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 8065
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1811
22.5%
l 990
12.3%
e 990
12.3%
f 990
12.3%
r 821
10.2%
i 821
10.2%
g 821
10.2%
h 821
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1811
22.5%
l 990
12.3%
e 990
12.3%
f 990
12.3%
r 821
10.2%
i 821
10.2%
g 821
10.2%
h 821
10.2%

Location1
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
ICA
1165 
ACA
336 
VABA
225 
MCA
194 

Length

Max length4
Median length3
Mean length3.1171875
Min length3

Characters and Unicode

Total characters5985
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowICA
2nd rowACA
3rd rowICA
4th rowICA
5th rowVABA

Common Values

ValueCountFrequency (%)
ICA 1165
60.7%
ACA 336
 
17.5%
VABA 225
 
11.7%
MCA 194
 
10.1%

Length

2023-09-16T15:33:01.631712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:01.809987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ica 1165
60.7%
aca 336
 
17.5%
vaba 225
 
11.7%
mca 194
 
10.1%

Most occurring characters

ValueCountFrequency (%)
A 2481
41.5%
C 1695
28.3%
I 1165
19.5%
V 225
 
3.8%
B 225
 
3.8%
M 194
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5985
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2481
41.5%
C 1695
28.3%
I 1165
19.5%
V 225
 
3.8%
B 225
 
3.8%
M 194
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5985
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2481
41.5%
C 1695
28.3%
I 1165
19.5%
V 225
 
3.8%
B 225
 
3.8%
M 194
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2481
41.5%
C 1695
28.3%
I 1165
19.5%
V 225
 
3.8%
B 225
 
3.8%
M 194
 
3.2%

VABAの区分
Categorical

Distinct2
Distinct (%)0.9%
Missing1695
Missing (%)88.3%
Memory size15.1 KiB
BA
170 
VA
55 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters450
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVA
2nd rowBA
3rd rowBA
4th rowBA
5th rowBA

Common Values

ValueCountFrequency (%)
BA 170
 
8.9%
VA 55
 
2.9%
(Missing) 1695
88.3%

Length

2023-09-16T15:33:01.967228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:02.127449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ba 170
75.6%
va 55
 
24.4%

Most occurring characters

ValueCountFrequency (%)
A 225
50.0%
B 170
37.8%
V 55
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 450
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 225
50.0%
B 170
37.8%
V 55
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 450
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 225
50.0%
B 170
37.8%
V 55
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 225
50.0%
B 170
37.8%
V 55
 
12.2%

Location2
Categorical

Distinct20
Distinct (%)1.2%
Missing235
Missing (%)12.2%
Memory size15.1 KiB
paraclinoid
358 
Pcom
302 
Acom
265 
Paraclinoid
232 
cavenous
144 
Other values (15)
384 

Length

Max length13
Median length11
Mean length7.3495549
Min length2

Characters and Unicode

Total characters12384
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowParaclinoid
2nd rowAcom
3rd rowPcom
4th rowPcom
5th rowPcom

Common Values

ValueCountFrequency (%)
paraclinoid 358
18.6%
Pcom 302
15.7%
Acom 265
13.8%
Paraclinoid 232
12.1%
cavenous 144
7.5%
top 92
 
4.8%
ant. choro 80
 
4.2%
distal 51
 
2.7%
BA-SCA 46
 
2.4%
bifurcation 33
 
1.7%
Other values (10) 82
 
4.3%
(Missing) 235
12.2%

Length

2023-09-16T15:33:02.283720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
paraclinoid 590
33.3%
pcom 302
17.1%
acom 265
15.0%
cavenous 144
 
8.1%
top 92
 
5.2%
ant 80
 
4.5%
choro 80
 
4.5%
distal 51
 
2.9%
ba-sca 46
 
2.6%
bifurcation 33
 
1.9%
Other values (10) 87
 
4.9%

Most occurring characters

ValueCountFrequency (%)
o 1602
12.9%
a 1504
12.1%
c 1432
11.6%
i 1303
10.5%
n 886
 
7.2%
r 741
 
6.0%
d 643
 
5.2%
l 641
 
5.2%
m 567
 
4.6%
P 554
 
4.5%
Other values (22) 2511
20.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10970
88.6%
Uppercase Letter 1157
 
9.3%
Space Separator 85
 
0.7%
Other Punctuation 80
 
0.6%
Dash Punctuation 63
 
0.5%
Decimal Number 29
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1602
14.6%
a 1504
13.7%
c 1432
13.1%
i 1303
11.9%
n 886
8.1%
r 741
6.8%
d 643
5.9%
l 641
5.8%
m 567
 
5.2%
p 450
 
4.1%
Other values (10) 1201
10.9%
Uppercase Letter
ValueCountFrequency (%)
P 554
47.9%
A 421
36.4%
C 66
 
5.7%
B 49
 
4.2%
S 46
 
4.0%
V 12
 
1.0%
I 9
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 21
72.4%
2 8
 
27.6%
Space Separator
ValueCountFrequency (%)
85
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12127
97.9%
Common 257
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1602
13.2%
a 1504
12.4%
c 1432
11.8%
i 1303
10.7%
n 886
 
7.3%
r 741
 
6.1%
d 643
 
5.3%
l 641
 
5.3%
m 567
 
4.7%
P 554
 
4.6%
Other values (17) 2254
18.6%
Common
ValueCountFrequency (%)
85
33.1%
. 80
31.1%
- 63
24.5%
1 21
 
8.2%
2 8
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1602
12.9%
a 1504
12.1%
c 1432
11.6%
i 1303
10.5%
n 886
 
7.2%
r 741
 
6.0%
d 643
 
5.2%
l 641
 
5.2%
m 567
 
4.6%
P 554
 
4.5%
Other values (22) 2511
20.3%

aneurysm neck
Real number (ℝ)

Distinct539
Distinct (%)28.2%
Missing11
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4.0335673
Minimum0.99
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:02.467008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile1.654
Q12.5
median3.32
Q34.62
95-th percentile8.7
Maximum27
Range26.01
Interquartile range (IQR)2.12

Descriptive statistics

Standard deviation2.710184
Coefficient of variation (CV)0.67190747
Kurtosis16.87959
Mean4.0335673
Median Absolute Deviation (MAD)1.01
Skewness3.3165688
Sum7700.08
Variance7.3450974
MonotonicityNot monotonic
2023-09-16T15:33:02.677886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 115
 
6.0%
2.5 104
 
5.4%
2 85
 
4.4%
3.5 78
 
4.1%
4 60
 
3.1%
4.5 43
 
2.2%
5 40
 
2.1%
1.5 28
 
1.5%
6 22
 
1.1%
5.5 21
 
1.1%
Other values (529) 1313
68.4%
ValueCountFrequency (%)
0.99 1
 
0.1%
1 6
0.3%
1.05 1
 
0.1%
1.06 1
 
0.1%
1.07 1
 
0.1%
1.1 2
 
0.1%
1.13 1
 
0.1%
1.14 1
 
0.1%
1.2 5
0.3%
1.26 1
 
0.1%
ValueCountFrequency (%)
27 1
0.1%
25.66 1
0.1%
25 1
0.1%
24.3 1
0.1%
24 1
0.1%
23 2
0.1%
20 2
0.1%
18.5 1
0.1%
18.3 1
0.1%
18.23 1
0.1%

aneurysm size1
Real number (ℝ)

Distinct717
Distinct (%)37.4%
Missing3
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.4505164
Minimum1.3
Maximum36.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:02.894458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2.6
Q13.78
median5
Q37.12
95-th percentile16.092
Maximum36.49
Range35.19
Interquartile range (IQR)3.34

Descriptive statistics

Standard deviation4.5825822
Coefficient of variation (CV)0.71042098
Kurtosis9.1343387
Mean6.4505164
Median Absolute Deviation (MAD)1.5
Skewness2.6588878
Sum12365.64
Variance21.000059
MonotonicityNot monotonic
2023-09-16T15:33:03.091250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 73
 
3.8%
4 73
 
3.8%
4.5 62
 
3.2%
3.5 56
 
2.9%
3 50
 
2.6%
5.5 47
 
2.4%
6 45
 
2.3%
7 38
 
2.0%
6.5 29
 
1.5%
8 22
 
1.1%
Other values (707) 1422
74.1%
ValueCountFrequency (%)
1.3 1
 
0.1%
1.5 1
 
0.1%
1.53 1
 
0.1%
1.55 1
 
0.1%
1.59 1
 
0.1%
1.7 2
 
0.1%
1.75 1
 
0.1%
1.85 1
 
0.1%
1.92 1
 
0.1%
2 8
0.4%
ValueCountFrequency (%)
36.49 1
0.1%
35.9 1
0.1%
34.5 1
0.1%
34.1 1
0.1%
34 1
0.1%
33.2 1
0.1%
32.45 1
0.1%
31 2
0.1%
29.57 1
0.1%
29.37 1
0.1%

aneurysm size2
Real number (ℝ)

Distinct706
Distinct (%)36.8%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.0994776
Minimum1
Maximum40.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:03.303686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.36
Q13.5
median4.6
Q37
95-th percentile15.5125
Maximum40.47
Range39.47
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation4.4526978
Coefficient of variation (CV)0.73001298
Kurtosis7.8643487
Mean6.0994776
Median Absolute Deviation (MAD)1.515
Skewness2.4730873
Sum11686.599
Variance19.826518
MonotonicityNot monotonic
2023-09-16T15:33:03.516219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 78
 
4.1%
4 70
 
3.6%
3.5 69
 
3.6%
4.5 68
 
3.5%
5 63
 
3.3%
6 39
 
2.0%
5.5 35
 
1.8%
2.5 34
 
1.8%
7 31
 
1.6%
6.5 26
 
1.4%
Other values (696) 1403
73.1%
ValueCountFrequency (%)
1 1
 
0.1%
1.2 1
 
0.1%
1.3 2
0.1%
1.43 1
 
0.1%
1.5 4
0.2%
1.51 1
 
0.1%
1.55 1
 
0.1%
1.56 1
 
0.1%
1.58 1
 
0.1%
1.63 1
 
0.1%
ValueCountFrequency (%)
40.47 1
0.1%
32 1
0.1%
31.06 1
0.1%
30 2
0.1%
29.64 1
0.1%
29.07 1
0.1%
28.7 1
0.1%
27.56 1
0.1%
27.19 1
0.1%
26.7 1
0.1%

aneurysm size3
Real number (ℝ)

Distinct486
Distinct (%)76.8%
Missing1287
Missing (%)67.0%
Infinite0
Infinite (%)0.0%
Mean9.2329147
Minimum1.3
Maximum33.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:03.753560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile3.091
Q14.715
median7.45
Q312
95-th percentile21.602
Maximum33.95
Range32.65
Interquartile range (IQR)7.285

Descriptive statistics

Standard deviation5.9931736
Coefficient of variation (CV)0.6491096
Kurtosis1.4988948
Mean9.2329147
Median Absolute Deviation (MAD)3.05
Skewness1.3411915
Sum5844.435
Variance35.918129
MonotonicityNot monotonic
2023-09-16T15:33:03.981204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 9
 
0.5%
3.5 9
 
0.5%
7.5 7
 
0.4%
4.75 7
 
0.4%
4.25 7
 
0.4%
4.5 7
 
0.4%
9 5
 
0.3%
5.25 5
 
0.3%
5 5
 
0.3%
3.75 5
 
0.3%
Other values (476) 567
29.5%
(Missing) 1287
67.0%
ValueCountFrequency (%)
1.3 1
0.1%
1.75 1
0.1%
2 1
0.1%
2.215 1
0.1%
2.25 1
0.1%
2.47 1
0.1%
2.49 1
0.1%
2.5 2
0.1%
2.54 1
0.1%
2.65 2
0.1%
ValueCountFrequency (%)
33.95 1
0.1%
33.1 1
0.1%
32 1
0.1%
30.5 1
0.1%
28.45 1
0.1%
28.17 1
0.1%
27.93 1
0.1%
27.705 1
0.1%
27.67 1
0.1%
27.5 1
0.1%

aneurysm volume
Real number (ℝ)

Distinct589
Distinct (%)30.7%
Missing3
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean337.61158
Minimum0
Maximum20410.921
Zeros1284
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:04.219270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351.716075
95-th percentile1959.5648
Maximum20410.921
Range20410.921
Interquartile range (IQR)51.716075

Descriptive statistics

Standard deviation1317.0585
Coefficient of variation (CV)3.9011057
Kurtosis68.817491
Mean337.61158
Median Absolute Deviation (MAD)0
Skewness7.1576631
Sum647201.4
Variance1734643
MonotonicityNot monotonic
2023-09-16T15:33:04.627942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1284
66.9%
21.98 7
 
0.4%
14.13 4
 
0.2%
27.475 4
 
0.2%
112.255 3
 
0.2%
86.35 3
 
0.2%
160.72875 3
 
0.2%
55.93125 3
 
0.2%
211.95 3
 
0.2%
65.41666667 3
 
0.2%
Other values (579) 600
31.2%
ValueCountFrequency (%)
0 1284
66.9%
1.149763333 1
 
0.1%
2.7475 1
 
0.1%
4.186666667 1
 
0.1%
5.633631 1
 
0.1%
5.8875 1
 
0.1%
7.33975 1
 
0.1%
7.885063333 1
 
0.1%
7.89118267 1
 
0.1%
8.00869874 1
 
0.1%
ValueCountFrequency (%)
20410.92107 1
0.1%
17081.6 1
0.1%
14844.35 1
0.1%
11575.77014 1
0.1%
11428.6806 1
0.1%
11088.72157 1
0.1%
11030.59348 1
0.1%
10331.24873 1
0.1%
10178.50625 1
0.1%
8745.7792 1
0.1%

再治療
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
無
1853 
有
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row有
2nd row無
3rd row有
4th row無
5th row無

Common Values

ValueCountFrequency (%)
無 1853
96.5%
有 67
 
3.5%

Length

2023-09-16T15:33:04.823570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:04.985322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
無 1853
96.5%
有 67
 
3.5%

Most occurring characters

ValueCountFrequency (%)
無 1853
96.5%
有 67
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1920
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
無 1853
96.5%
有 67
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Han 1920
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
無 1853
96.5%
有 67
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
CJK 1920
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
無 1853
96.5%
有 67
 
3.5%

治療回数
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
1
1917 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1917
99.8%
2 3
 
0.2%

Length

2023-09-16T15:33:05.119172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:05.274004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1917
99.8%
2 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 1917
99.8%
2 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1920
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1917
99.8%
2 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1917
99.8%
2 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1917
99.8%
2 3
 
0.2%

VER
Real number (ℝ)

Distinct161
Distinct (%)83.4%
Missing1727
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean34.072487
Minimum11.9
Maximum70.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:05.428495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11.9
5-th percentile19.12
Q126.7
median32.5
Q339.1
95-th percentile57.46
Maximum70.58
Range58.68
Interquartile range (IQR)12.4

Descriptive statistics

Standard deviation11.068139
Coefficient of variation (CV)0.32484093
Kurtosis1.0019214
Mean34.072487
Median Absolute Deviation (MAD)6.2
Skewness0.82883973
Sum6575.99
Variance122.50369
MonotonicityNot monotonic
2023-09-16T15:33:05.625160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.8 4
 
0.2%
25.2 3
 
0.2%
24.5 3
 
0.2%
36.5 3
 
0.2%
25.8 3
 
0.2%
27.5 2
 
0.1%
33.7 2
 
0.1%
33.8 2
 
0.1%
35 2
 
0.1%
26.5 2
 
0.1%
Other values (151) 167
 
8.7%
(Missing) 1727
89.9%
ValueCountFrequency (%)
11.9 1
0.1%
12.89 1
0.1%
12.9 1
0.1%
14.2 1
0.1%
14.21 1
0.1%
15 1
0.1%
16.1 1
0.1%
17.3 1
0.1%
17.5 1
0.1%
18.7 1
0.1%
ValueCountFrequency (%)
70.58 1
0.1%
69.7 1
0.1%
66.4 1
0.1%
64.5 1
0.1%
60.8 1
0.1%
60.4 1
0.1%
59.5 1
0.1%
59 1
0.1%
58.8 1
0.1%
58.6 1
0.1%

outcome
Categorical

Distinct3
Distinct (%)0.2%
Missing361
Missing (%)18.8%
Memory size15.1 KiB
Complete Occlusion
779 
Body filling
484 
Neck remnant
296 

Length

Max length18
Median length12
Mean length14.998076
Min length12

Characters and Unicode

Total characters23382
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBody filling
2nd rowBody filling
3rd rowBody filling
4th rowComplete Occlusion
5th rowComplete Occlusion

Common Values

ValueCountFrequency (%)
Complete Occlusion 779
40.6%
Body filling 484
25.2%
Neck remnant 296
 
15.4%
(Missing) 361
18.8%

Length

2023-09-16T15:33:05.809256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:33:05.985187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
complete 779
25.0%
occlusion 779
25.0%
body 484
15.5%
filling 484
15.5%
neck 296
 
9.5%
remnant 296
 
9.5%

Most occurring characters

ValueCountFrequency (%)
l 2526
 
10.8%
e 2150
 
9.2%
o 2042
 
8.7%
n 1855
 
7.9%
c 1854
 
7.9%
i 1747
 
7.5%
1559
 
6.7%
m 1075
 
4.6%
t 1075
 
4.6%
C 779
 
3.3%
Other values (13) 6720
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19485
83.3%
Uppercase Letter 2338
 
10.0%
Space Separator 1559
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 2526
13.0%
e 2150
11.0%
o 2042
10.5%
n 1855
9.5%
c 1854
9.5%
i 1747
9.0%
m 1075
 
5.5%
t 1075
 
5.5%
s 779
 
4.0%
u 779
 
4.0%
Other values (8) 3603
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 779
33.3%
O 779
33.3%
B 484
20.7%
N 296
 
12.7%
Space Separator
ValueCountFrequency (%)
1559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21823
93.3%
Common 1559
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 2526
11.6%
e 2150
 
9.9%
o 2042
 
9.4%
n 1855
 
8.5%
c 1854
 
8.5%
i 1747
 
8.0%
m 1075
 
4.9%
t 1075
 
4.9%
C 779
 
3.6%
s 779
 
3.6%
Other values (12) 5941
27.2%
Common
ValueCountFrequency (%)
1559
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 2526
 
10.8%
e 2150
 
9.2%
o 2042
 
8.7%
n 1855
 
7.9%
c 1854
 
7.9%
i 1747
 
7.5%
1559
 
6.7%
m 1075
 
4.6%
t 1075
 
4.6%
C 779
 
3.3%
Other values (13) 6720
28.7%

adjunc tech.
Categorical

Distinct24
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
BAT
501 
Stent assist
447 
Simple
340 
Flow Diverter
235 
FD + Coil
101 
Other values (19)
296 

Length

Max length21
Median length16
Mean length8.596875
Min length1

Characters and Unicode

Total characters16506
Distinct characters46
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.3%

Sample

1st rowSimpleからBAT
2nd rowDouble cathe (DC)
3rd rowSimple
4th rowSimple
5th rowDouble cathe (DC)

Common Values

ValueCountFrequency (%)
BAT 501
26.1%
Stent assist 447
23.3%
Simple 340
17.7%
Flow Diverter 235
12.2%
FD + Coil 101
 
5.3%
Double cathe (DC) 94
 
4.9%
BATからStent 56
 
2.9%
Stent内からBAT 31
 
1.6%
Y Stent 28
 
1.5%
DAC+Simple 27
 
1.4%
Other values (14) 60
 
3.1%

Length

2023-09-16T15:33:06.187898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bat 506
16.6%
stent 483
15.8%
assist 447
14.6%
simple 357
11.7%
diverter 236
7.7%
flow 235
7.7%
108
 
3.5%
fd 101
 
3.3%
coil 101
 
3.3%
dc 99
 
3.2%
Other values (18) 384
12.6%

Most occurring characters

ValueCountFrequency (%)
t 1935
 
11.7%
e 1649
 
10.0%
s 1349
 
8.2%
i 1178
 
7.1%
1137
 
6.9%
S 970
 
5.9%
l 859
 
5.2%
A 637
 
3.9%
B 628
 
3.8%
T 611
 
3.7%
Other values (36) 5553
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10717
64.9%
Uppercase Letter 4035
 
24.4%
Space Separator 1137
 
6.9%
Other Letter 291
 
1.8%
Math Symbol 129
 
0.8%
Close Punctuation 94
 
0.6%
Open Punctuation 94
 
0.6%
Other Punctuation 8
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1935
18.1%
e 1649
15.4%
s 1349
12.6%
i 1178
11.0%
l 859
8.0%
n 594
 
5.5%
a 560
 
5.2%
r 473
 
4.4%
o 465
 
4.3%
p 395
 
3.7%
Other values (7) 1260
11.8%
Uppercase Letter
ValueCountFrequency (%)
S 970
24.0%
A 637
15.8%
B 628
15.6%
T 611
15.1%
D 572
14.2%
F 337
 
8.4%
C 242
 
6.0%
Y 28
 
0.7%
R 8
 
0.2%
W 1
 
< 0.1%
Other Letter
ValueCountFrequency (%)
か 111
38.1%
ら 111
38.1%
内 31
 
10.7%
待 17
 
5.8%
機 17
 
5.8%
記 1
 
0.3%
可 1
 
0.3%
不 1
 
0.3%
入 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
? 2
 
25.0%
. 1
 
12.5%
Math Symbol
ValueCountFrequency (%)
+ 128
99.2%
→ 1
 
0.8%
Space Separator
ValueCountFrequency (%)
1137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 94
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14752
89.4%
Common 1463
 
8.9%
Hiragana 222
 
1.3%
Han 69
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1935
13.1%
e 1649
 
11.2%
s 1349
 
9.1%
i 1178
 
8.0%
S 970
 
6.6%
l 859
 
5.8%
A 637
 
4.3%
B 628
 
4.3%
T 611
 
4.1%
n 594
 
4.0%
Other values (18) 4342
29.4%
Common
ValueCountFrequency (%)
1137
77.7%
+ 128
 
8.7%
) 94
 
6.4%
( 94
 
6.4%
& 5
 
0.3%
? 2
 
0.1%
→ 1
 
0.1%
- 1
 
0.1%
. 1
 
0.1%
Han
ValueCountFrequency (%)
内 31
44.9%
待 17
24.6%
機 17
24.6%
記 1
 
1.4%
可 1
 
1.4%
不 1
 
1.4%
入 1
 
1.4%
Hiragana
ValueCountFrequency (%)
か 111
50.0%
ら 111
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16214
98.2%
Hiragana 222
 
1.3%
CJK 69
 
0.4%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1935
 
11.9%
e 1649
 
10.2%
s 1349
 
8.3%
i 1178
 
7.3%
1137
 
7.0%
S 970
 
6.0%
l 859
 
5.3%
A 637
 
3.9%
B 628
 
3.9%
T 611
 
3.8%
Other values (26) 5261
32.4%
Hiragana
ValueCountFrequency (%)
か 111
50.0%
ら 111
50.0%
CJK
ValueCountFrequency (%)
内 31
44.9%
待 17
24.6%
機 17
24.6%
記 1
 
1.4%
可 1
 
1.4%
不 1
 
1.4%
入 1
 
1.4%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

治療時合併症
Categorical

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
none
1714 
Ischemic complication
 
109
Hemorrhagic complication
 
39
Others
 
21
Aneurysm rupture in post treatment　
 
9
Other values (9)
 
28

Length

Max length57
Median length4
Mean length5.8963542
Min length4

Characters and Unicode

Total characters11321
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowCranial nerve palsy
2nd rowProcedure related complication
3rd rowAneurysm rupture in post treatment　
4th rowAneurysm rupture in post treatment　
5th rowCranial nerve palsy

Common Values

ValueCountFrequency (%)
none 1714
89.3%
Ischemic complication 109
 
5.7%
Hemorrhagic complication 39
 
2.0%
Others 21
 
1.1%
Aneurysm rupture in post treatment　 9
 
0.5%
Cranial nerve palsy 8
 
0.4%
Puncture site 7
 
0.4%
Hemorrhagic complication/Ischemic complication 4
 
0.2%
Procedure related complication 3
 
0.2%
Procedure related complication/Ischemic complication 2
 
0.1%
Other values (4) 4
 
0.2%

Length

2023-09-16T15:33:06.397353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 1714
79.4%
complication 159
 
7.4%
ischemic 113
 
5.2%
hemorrhagic 43
 
2.0%
others 21
 
1.0%
aneurysm 10
 
0.5%
rupture 10
 
0.5%
in 10
 
0.5%
post 10
 
0.5%
treatment 10
 
0.5%
Other values (11) 60
 
2.8%

Most occurring characters

ValueCountFrequency (%)
n 3651
32.2%
o 2109
18.6%
e 1994
17.6%
c 630
 
5.6%
i 524
 
4.6%
m 350
 
3.1%
t 260
 
2.3%
a 254
 
2.2%
239
 
2.1%
p 197
 
1.7%
Other values (17) 1113
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10846
95.8%
Space Separator 249
 
2.2%
Uppercase Letter 216
 
1.9%
Other Punctuation 10
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3651
33.7%
o 2109
19.4%
e 1994
18.4%
c 630
 
5.8%
i 524
 
4.8%
m 350
 
3.2%
t 260
 
2.4%
a 254
 
2.3%
p 197
 
1.8%
l 192
 
1.8%
Other values (8) 685
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
I 119
55.1%
H 43
 
19.9%
O 22
 
10.2%
P 13
 
6.0%
A 10
 
4.6%
C 9
 
4.2%
Space Separator
ValueCountFrequency (%)
239
96.0%
　 10
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11062
97.7%
Common 259
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3651
33.0%
o 2109
19.1%
e 1994
18.0%
c 630
 
5.7%
i 524
 
4.7%
m 350
 
3.2%
t 260
 
2.4%
a 254
 
2.3%
p 197
 
1.8%
l 192
 
1.7%
Other values (14) 901
 
8.1%
Common
ValueCountFrequency (%)
239
92.3%
　 10
 
3.9%
/ 10
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11311
99.9%
None 10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3651
32.3%
o 2109
18.6%
e 1994
17.6%
c 630
 
5.6%
i 524
 
4.6%
m 350
 
3.1%
t 260
 
2.3%
a 254
 
2.2%
239
 
2.1%
p 197
 
1.7%
Other values (16) 1103
 
9.8%
None
ValueCountFrequency (%)
　 10
100.0%

治療時合併症コメント
Categorical

HIGH CARDINALITY  MISSING 

Distinct54
Distinct (%)26.2%
Missing1714
Missing (%)89.3%
Memory size15.1 KiB
CT MRIで描出される脳梗塞
57 
術中破裂
20 
ステント内血栓症 無症候性
11 
出血を伴う分岐損傷
 
9
CT MRI で描出される脳梗塞
 
8
Other values (49)
101 

Length

Max length36
Median length33
Mean length12.936893
Min length2

Characters and Unicode

Total characters2665
Distinct characters159
Distinct categories8 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)12.1%

Sample

1st row脳神経麻痺出現増悪
2nd rowコイル突出
3rd row術後破裂
4th row術後破裂
5th rowCT MRIで抽出される脳梗塞

Common Values

ValueCountFrequency (%)
CT MRIで描出される脳梗塞 57
 
3.0%
術中破裂 20
 
1.0%
ステント内血栓症 無症候性 11
 
0.6%
出血を伴う分岐損傷 9
 
0.5%
CT MRI で描出される脳梗塞 8
 
0.4%
ステント内血栓症 症候性 8
 
0.4%
術後破裂 7
 
0.4%
脳神経麻痺出現増悪 6
 
0.3%
頭蓋外親動脈損傷 5
 
0.3%
Angioで描出される脳梗塞　分岐閉塞 5
 
0.3%
Other values (44) 70
 
3.6%
(Missing) 1714
89.3%

Length

2023-09-16T15:33:06.595195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ct 68
19.8%
mriで描出される脳梗塞 59
17.2%
ステント内血栓症 28
 
8.1%
術中破裂 20
 
5.8%
で描出される脳梗塞 20
 
5.8%
mri 14
 
4.1%
無症候性 11
 
3.2%
出血を伴う分岐損傷 9
 
2.6%
分岐閉塞 8
 
2.3%
症候性 8
 
2.3%
Other values (48) 99
28.8%

Most occurring characters

ValueCountFrequency (%)
129
 
4.8%
出 123
 
4.6%
塞 112
 
4.2%
る 108
 
4.1%
脳 106
 
4.0%
で 93
 
3.5%
梗 92
 
3.5%
さ 92
 
3.5%
れ 92
 
3.5%
描 91
 
3.4%
Other values (149) 1627
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2024
75.9%
Uppercase Letter 411
 
15.4%
Space Separator 138
 
5.2%
Lowercase Letter 64
 
2.4%
Other Punctuation 20
 
0.8%
Modifier Letter 6
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
出 123
 
6.1%
塞 112
 
5.5%
る 108
 
5.3%
脳 106
 
5.2%
で 93
 
4.6%
梗 92
 
4.5%
さ 92
 
4.5%
れ 92
 
4.5%
描 91
 
4.5%
血 64
 
3.2%
Other values (128) 1051
51.9%
Uppercase Letter
ValueCountFrequency (%)
T 76
18.5%
C 76
18.5%
I 76
18.5%
R 76
18.5%
M 76
18.5%
A 17
 
4.1%
F 6
 
1.5%
D 6
 
1.5%
H 1
 
0.2%
S 1
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
o 16
25.0%
i 16
25.0%
g 16
25.0%
n 16
25.0%
Space Separator
ValueCountFrequency (%)
129
93.5%
　 9
 
6.5%
Other Punctuation
ValueCountFrequency (%)
/ 19
95.0%
、 1
 
5.0%
Modifier Letter
ValueCountFrequency (%)
ー 6
100.0%
Open Punctuation
ValueCountFrequency (%)
（ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
） 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 1393
52.3%
Latin 475
 
17.8%
Hiragana 450
 
16.9%
Katakana 181
 
6.8%
Common 166
 
6.2%

Most frequent character per script

Han
ValueCountFrequency (%)
出 123
 
8.8%
塞 112
 
8.0%
脳 106
 
7.6%
梗 92
 
6.6%
描 91
 
6.5%
血 64
 
4.6%
症 56
 
4.0%
性 37
 
2.7%
内 34
 
2.4%
分 34
 
2.4%
Other values (96) 644
46.2%
Katakana
ValueCountFrequency (%)
ン 38
21.0%
テ 35
19.3%
ト 32
17.7%
ス 32
17.7%
ル 8
 
4.4%
コ 8
 
4.4%
イ 6
 
3.3%
ア 4
 
2.2%
グ 4
 
2.2%
レ 3
 
1.7%
Other values (7) 11
 
6.1%
Hiragana
ValueCountFrequency (%)
る 108
24.0%
で 93
20.7%
さ 92
20.4%
れ 92
20.4%
を 20
 
4.4%
す 15
 
3.3%
う 12
 
2.7%
に 9
 
2.0%
と 3
 
0.7%
が 1
 
0.2%
Other values (5) 5
 
1.1%
Latin
ValueCountFrequency (%)
T 76
16.0%
C 76
16.0%
I 76
16.0%
R 76
16.0%
M 76
16.0%
A 17
 
3.6%
o 16
 
3.4%
i 16
 
3.4%
g 16
 
3.4%
n 16
 
3.4%
Other values (4) 14
 
2.9%
Common
ValueCountFrequency (%)
129
77.7%
/ 19
 
11.4%
　 9
 
5.4%
ー 6
 
3.6%
、 1
 
0.6%
（ 1
 
0.6%
） 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
CJK 1393
52.3%
ASCII 623
23.4%
Hiragana 450
 
16.9%
Katakana 187
 
7.0%
None 12
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
129
20.7%
T 76
12.2%
C 76
12.2%
I 76
12.2%
R 76
12.2%
M 76
12.2%
/ 19
 
3.0%
A 17
 
2.7%
o 16
 
2.6%
i 16
 
2.6%
Other values (6) 46
 
7.4%
CJK
ValueCountFrequency (%)
出 123
 
8.8%
塞 112
 
8.0%
脳 106
 
7.6%
梗 92
 
6.6%
描 91
 
6.5%
血 64
 
4.6%
症 56
 
4.0%
性 37
 
2.7%
内 34
 
2.4%
分 34
 
2.4%
Other values (96) 644
46.2%
Hiragana
ValueCountFrequency (%)
る 108
24.0%
で 93
20.7%
さ 92
20.4%
れ 92
20.4%
を 20
 
4.4%
す 15
 
3.3%
う 12
 
2.7%
に 9
 
2.0%
と 3
 
0.7%
が 1
 
0.2%
Other values (5) 5
 
1.1%
Katakana
ValueCountFrequency (%)
ン 38
20.3%
テ 35
18.7%
ト 32
17.1%
ス 32
17.1%
ル 8
 
4.3%
コ 8
 
4.3%
ー 6
 
3.2%
イ 6
 
3.2%
ア 4
 
2.1%
グ 4
 
2.1%
Other values (8) 14
 
7.5%
None
ValueCountFrequency (%)
　 9
75.0%
、 1
 
8.3%
（ 1
 
8.3%
） 1
 
8.3%

19動脈瘤データ（An ID 1）::bleb
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1781 
True
 
139
ValueCountFrequency (%)
False 1781
92.8%
True 139
 
7.2%
2023-09-16T15:33:06.793824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

23動脈瘤データ_Ver2（An ID 1）::術中Date of rupture
Categorical

MISSING  UNIFORM 

Distinct23
Distinct (%)95.8%
Missing1896
Missing (%)98.8%
Memory size15.1 KiB
2018/7/31
2 
2004/12/25
 
1
2019/8/2
 
1
2019/3/29
 
1
2017/9/19
 
1
Other values (18)
18 

Length

Max length10
Median length9
Mean length8.8333333
Min length8

Characters and Unicode

Total characters212
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)91.7%

Sample

1st row2004/12/25
2nd row2005/4/12
3rd row2008/9/30
4th row2009/8/11
5th row2009/9/15

Common Values

ValueCountFrequency (%)
2018/7/31 2
 
0.1%
2004/12/25 1
 
0.1%
2019/8/2 1
 
0.1%
2019/3/29 1
 
0.1%
2017/9/19 1
 
0.1%
2017/6/27 1
 
0.1%
2016/9/6 1
 
0.1%
2016/4/12 1
 
0.1%
2016/1/12 1
 
0.1%
2015/9/15 1
 
0.1%
Other values (13) 13
 
0.7%
(Missing) 1896
98.8%

Length

2023-09-16T15:33:06.953281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018/7/31 2
 
8.3%
2013/4/9 1
 
4.2%
2005/4/12 1
 
4.2%
2008/9/30 1
 
4.2%
2009/8/11 1
 
4.2%
2009/9/15 1
 
4.2%
2010/6/1 1
 
4.2%
2010/9/7 1
 
4.2%
2011/5/31 1
 
4.2%
2012/7/24 1
 
4.2%
Other values (13) 13
54.2%

Most occurring characters

ValueCountFrequency (%)
/ 48
22.6%
1 40
18.9%
2 35
16.5%
0 33
15.6%
9 15
 
7.1%
7 8
 
3.8%
3 8
 
3.8%
5 7
 
3.3%
8 6
 
2.8%
4 6
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164
77.4%
Other Punctuation 48
 
22.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 40
24.4%
2 35
21.3%
0 33
20.1%
9 15
 
9.1%
7 8
 
4.9%
3 8
 
4.9%
5 7
 
4.3%
8 6
 
3.7%
4 6
 
3.7%
6 6
 
3.7%
Other Punctuation
ValueCountFrequency (%)
/ 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 212
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 48
22.6%
1 40
18.9%
2 35
16.5%
0 33
15.6%
9 15
 
7.1%
7 8
 
3.8%
3 8
 
3.8%
5 7
 
3.3%
8 6
 
2.8%
4 6
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 48
22.6%
1 40
18.9%
2 35
16.5%
0 33
15.6%
9 15
 
7.1%
7 8
 
3.8%
3 8
 
3.8%
5 7
 
3.3%
8 6
 
2.8%
4 6
 
2.8%

23動脈瘤データ_Ver2（An ID 1）::術中Rupture
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)4.2%
Missing1896
Missing (%)98.8%
Memory size3.9 KiB
True
 
24
(Missing)
1896 
ValueCountFrequency (%)
True 24
 
1.2%
(Missing) 1896
98.8%
2023-09-16T15:33:07.110804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

total length
Real number (ℝ)

Distinct339
Distinct (%)20.2%
Missing244
Missing (%)12.7%
Infinite0
Infinite (%)0.0%
Mean65.786038
Minimum2
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-09-16T15:33:07.238038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q120
median35
Q369
95-th percentile204.5
Maximum1460
Range1458
Interquartile range (IQR)49

Descriptive statistics

Standard deviation101.94988
Coefficient of variation (CV)1.5497191
Kurtosis42.054862
Mean65.786038
Median Absolute Deviation (MAD)20
Skewness5.3357885
Sum110257.4
Variance10393.777
MonotonicityNot monotonic
2023-09-16T15:33:07.421757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 38
 
2.0%
20 36
 
1.9%
15 35
 
1.8%
14 34
 
1.8%
18 33
 
1.7%
25 32
 
1.7%
33 30
 
1.6%
24 30
 
1.6%
16 28
 
1.5%
21 28
 
1.5%
Other values (329) 1352
70.4%
(Missing) 244
 
12.7%
ValueCountFrequency (%)
2 1
 
0.1%
2.5 1
 
0.1%
3 7
0.4%
3.5 2
 
0.1%
4 13
0.7%
5 9
0.5%
5.5 2
 
0.1%
6 15
0.8%
7 15
0.8%
7.5 1
 
0.1%
ValueCountFrequency (%)
1460 1
0.1%
1012 1
0.1%
964 1
0.1%
935 1
0.1%
780 1
0.1%
745 1
0.1%
704 2
0.1%
689 1
0.1%
635 1
0.1%
629 1
0.1%

Unnamed: 28
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 29
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 30
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 31
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 32
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 33
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 34
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 35
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 36
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 37
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 38
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 39
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 40
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Unnamed: 41
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1920
Missing (%)100.0%
Memory size15.1 KiB

Interactions

2023-09-16T15:32:53.229310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.090507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.953088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:36.677596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:39.272141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:41.823753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:44.225638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:46.609942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:49.246999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:51.553990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:53.416834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.230298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:35.022093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:37.161243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:39.501172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:42.046874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:44.454929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:46.967380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:49.554379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:51.699607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:53.625618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.330498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:35.110781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:37.436839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:39.768442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:42.279329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:44.704485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:47.298016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:49.989592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:51.862992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:53.811149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.413818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:35.385915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:37.700876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:40.079040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:42.534862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:44.924152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:47.573831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:50.191205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:52.045244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:53.978806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.524892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:35.512444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:37.931452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:40.325595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:42.778917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:45.159696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:47.821021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:50.376778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:52.241776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:54.165518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.610893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:35.655477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:38.181480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:40.570637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:42.961904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:45.373131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:48.056813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:50.600412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:52.420681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:54.376281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.686450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:35.792994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:38.426042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:40.806687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:43.170955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:45.669449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:48.297910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:50.806443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:52.607220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:54.579961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.753274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:35.902506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:38.654667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:41.052271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:43.555938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:45.932394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:48.534780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:50.998923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:52.747896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:54.948381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.819390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:36.134057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:38.880720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:41.318075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:43.770955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:46.181285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:48.796103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:51.203716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:52.916723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:55.111253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:34.881379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:36.412124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:39.048111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:41.562837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:44.021208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:46.348521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:48.973421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:51.366599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:53.072389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2023-09-16T15:32:55.500526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T15:32:57.195069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-16T15:32:57.855183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

患者IDAn ID1検査日検査種別08個人情報(本院)::性別年齢An status 119動脈瘤データ（An ID 1）::Aneurysm Type2SideLocation1VABAの区分Location2aneurysm neckaneurysm size1aneurysm size2aneurysm size3aneurysm volume再治療治療回数VERoutcomeadjunc tech.治療時合併症治療時合併症コメント19動脈瘤データ（An ID 1）::bleb23動脈瘤データ_Ver2（An ID 1）::術中Date of rupture23動脈瘤データ_Ver2（An ID 1）::術中Rupturetotal lengthUnnamed: 28Unnamed: 29Unnamed: 30Unnamed: 31Unnamed: 32Unnamed: 33Unnamed: 34Unnamed: 35Unnamed: 36Unnamed: 37Unnamed: 38Unnamed: 39Unnamed: 40Unnamed: 41
05174866130732003/5/14Vascular IVR女68unrupturesaccularrightICANaNParaclinoid6.520.013.016.502245.100000有1NaNBody fillingSimpleからBATCranial nerve palsy脳神経麻痺出現増悪noNaNNaN300.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1291865130742003/7/9Vascular IVR女68unrupturesaccularNaNACANaNAcom5.06.06.06.00113.040000無1NaNBody fillingDouble cathe (DC)Procedure related complicationコイル突出noNaNNaN40.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
25293880130752003/7/11Vascular IVR女63unrupturesaccularrightICANaNPcom6.08.04.06.00100.480000有1NaNBody fillingSimpleAneurysm rupture in post treatment術後破裂noNaNNaN108.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
35432597130762003/11/28Vascular IVR女70unrupturesaccularleftICANaNPcom3.77.67.87.70238.878640無1NaNComplete OcclusionSimpleAneurysm rupture in post treatment術後破裂noNaNNaN70.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
45736867130772004/10/19Vascular IVR女57unrupturesaccularrightVABAVANaN6.010.08.09.00376.800000無1NaNNaNDouble cathe (DC)Cranial nerve palsyCT MRIで抽出される脳梗塞noNaNNaN252.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
55442513130782004/12/25Vascular IVR女46unrupturesaccularleftICANaNPcom3.612.18.510.30554.395783無1NaNComplete OcclusionSimpleHemorrhagic complication術中破裂no2004/12/25yes150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
65984991130792005/4/5Vascular IVR女60unrupturesaccularleftICANaNParaclinoid3.55.04.54.7555.931250無1NaNNeck remnantSimpleIschemic complication網膜動脈分岐閉塞noNaNNaN66.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
75994043130802005/4/12Vascular IVR男55unrupturesaccularrightACANaNA1-A24.17.57.37.40212.028500無1NaNComplete OcclusionSimpleHemorrhagic complication術中破裂no2005/4/12yes125.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
82808377130812005/7/27Vascular IVR女47unrupturesaccularNaNVABABAtop5.55.05.05.0065.416667有1NaNBody fillingBATAneurysm rupture in post treatment術後破裂noNaNNaN40.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
96191275130822005/11/22Vascular IVR女64unrupturesaccularleftACANaNdistal2.53.53.03.2517.858750無1NaNBody fillingSimpleHemorrhagic complication出血を伴う分岐損傷noNaNNaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
患者IDAn ID1検査日検査種別08個人情報(本院)::性別年齢An status 119動脈瘤データ（An ID 1）::Aneurysm Type2SideLocation1VABAの区分Location2aneurysm neckaneurysm size1aneurysm size2aneurysm size3aneurysm volume再治療治療回数VERoutcomeadjunc tech.治療時合併症治療時合併症コメント19動脈瘤データ（An ID 1）::bleb23動脈瘤データ_Ver2（An ID 1）::術中Date of rupture23動脈瘤データ_Ver2（An ID 1）::術中Rupturetotal lengthUnnamed: 28Unnamed: 29Unnamed: 30Unnamed: 31Unnamed: 32Unnamed: 33Unnamed: 34Unnamed: 35Unnamed: 36Unnamed: 37Unnamed: 38Unnamed: 39Unnamed: 40Unnamed: 41
19107620096149922021/9/14Vascular IVR女74unrupturesaccularrightICANaNPcom1.554.444.32NaN0.0無1NaNNeck remnantDAC+SimplenoneNaNnoNaNNaN20.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
191112238492149932021/9/14Vascular IVR女63unrupturesaccularrightMCANaNNaN2.971.753.58NaN0.0無1NaNComplete OcclusionStent assistnoneNaNnoNaNNaN17.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
19124100868149942021/9/14Vascular IVR男72unrupturesaccularleftACANaNAcom1.293.202.19NaN0.0無1NaNBody fillingDAC+SimplenoneNaNnoNaNNaN6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
191313056952149952021/9/24Vascular IVR女42unrupturesaccularleftICANaNant. choro2.004.051.92NaN0.0無1NaNNeck remnantDAC+SimplenoneNaNnoNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
191413051494149962021/9/24Vascular IVR男45unrupturesaccularrightMCANaNNaN1.884.103.16NaN0.0無1NaNBody fillingSimplenoneNaNnoNaNNaN22.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
191513039936149972021/9/28Vascular IVR女29unrupturesaccularrightICANaNparaclinoid2.992.993.92NaN0.0無1NaNNaNStent assistnoneNaNnoNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
19168009095149982009/8/18Vascular IVR女49unrupturesaccularrightMCANaNNaN2.504.003.00NaN0.0無1NaNNeck remnantDouble cathe (DC)noneNaNnoNaNNaN17.5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
19178589654149992010/12/21Vascular IVR女50unrupturesaccularleftICANaNparaclinoid1.503.503.00NaN0.0無1NaNNeck remnantBATnoneNaNnoNaNNaN16.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
19189083984150012011/12/6Vascular IVR女60unrupturesaccularleftVABAVAVA6.008.507.50NaN0.0無1NaNComplete OcclusionStent assistnoneNaNnoNaNNaN166.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
19199646332150022013/3/12Vascular IVR女72unrupturesaccularrightICANaNPcom2.344.934.23NaN0.0無1NaNNeck remnantDAC+SimplenoneNaNnoNaNNaN35.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN